发表机构
University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对持续学习中的灾难性遗忘，提出基于轨迹调节的规范导航方法 TMLN，利用经验 Fisher 信息矩阵与历史轨迹动态预 conditioning，显著降低任务间损失障碍。
AI 中文摘要
持续学习模型在非平稳数据分布上按顺序训练时，会遭受灾难性遗忘。此前，这一问题已通过权重正则化加以解决。虽然梯度预 conditioning 为缓解遗忘提供了有前景的替代方案，但当前方法具有短视性。相反，标准正则化方法施加刚性、标量的欧几里得惩罚，完全忽略了参数空间底层的黎曼几何结构。为克服这一不足，我们提出 TMLN（轨迹调节景观导航），一种规范性导航策略，将持续学习形式化为弯曲损失景观上的最优控制问题。TMLN 利用内存高效的逐对角线经验 Fisher 信息矩阵（FIM）来定义局部黎曼流形。为补偿对角近似的空间局限性，TMLN 使用网络参数值的归一化历史轨迹动态调节预 conditioning 器。通过将这种基于轨迹的预 conditioning 直接集成到梯度更新中，我们主动保护历史上关键的参数方向，而无需依赖加性惩罚。在类增量和域增量基准上的实证评估表明，我们的方法显著降低了连续任务之间的损失障碍。
英文摘要
Continual learning models suffer from catastrophic forgetting when trained sequentially on non-stationary data distributions. Previously, this has been addressed through weight regularization. While preconditioning gradients offer a promising alternative to mitigate forgetting, current approaches are myopic. Conversely, standard regularization methods apply rigid, scalar Euclidean penalties that entirely ignore the underlying Riemannian geometry of the parameter space. To overcome this gap, we propose TMLN (Trajectory-Modulatory Landscape Navigation), a normative navigation policy that formalizes continual learning as an optimal control problem over a curved loss landscape. TMLN utilizes a memory-efficient diagonal empirical Fisher Information Matrix (FIM) to define a localized Riemannian manifold. To compensate for the spatial limitations of the diagonal approximation, TMLN dynamically modulates a preconditioner using the normalized historical trajectory of the network's parameter values. By integrating this trajectory-based preconditioning directly into the gradient update, we actively shield historically critical parameter directions without relying on additive penalties. Empirical evaluations on class- and domain-incremental benchmarks demonstrate that our method significantly reduces the loss barrier between consecutive tasks.
